Distinct Regions of Right Temporo-Parietal Junction Are
Selective for Theory of Mind and Exogenous Attention
Jonathan Scholz1, Christina Triantafyllou2, Susan Whitfield-Gabrieli2, Emery N. Brown2, Rebecca Saxe2*
1 School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America, 2 Department of Brain and Cognitive Sciences, Massachusetts
Institute of Technology, Cambridge, Massachusetts, United States of America
Abstract
In functional magnetic resonance imaging (fMRI) studies, a cortical region in the right temporo-parietal junction (RTPJ) is
recruited when participants read stories about people’s thoughts (‘Theory of Mind’). Both fMRI and lesion studies suggest
that a region near the RTPJ is associated with attentional reorienting in response to an unexpected stimulus. Do Theory of
Mind and attentional reorienting recruit a single population of neurons, or are there two neighboring but distinct neural
populations in the RTPJ? One recent study compared these activations, and found evidence consistent with a single
common region. However, the apparent overlap may have been due to the low resolution of the previous technique. We
tested this hypothesis using a high-resolution protocol, within-subjects analyses, and more powerful statistical methods.
Strict conjunction analyses revealed that the area of overlap was small and on the periphery of each activation. In addition, a
bootstrap analysis identified a reliable 6–10 mm spatial displacement between the peak activations of the two tasks; the
same magnitude and direction of displacement was observed in within-subjects comparisons. In all, these results suggest
that there are neighboring but distinct regions within the RTPJ implicated in Theory of Mind and orienting attention.
Citation: Scholz J, Triantafyllou C, Whitfield-Gabrieli S, Brown EN, Saxe R (2009) Distinct Regions of Right Temporo-Parietal Junction Are Selective for Theory of
Mind and Exogenous Attention. PLoS ONE 4(3): e4869. doi:10.1371/journal.pone.0004869
Editor: Jan Lauwereyns, Victoria University of Wellington, New Zealand
Received November 16, 2008; Accepted November 26, 2008; Published March 17, 2009
Copyright: ! 2009 Scholz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: RS was supported by a John Merck fellowship and grants from the Ellison Medical Foundation, the Packard Foundation and the Simons Foundation. EB
was supported by R01 DA015644. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: saxe@mit.edu
Introduction
engaged. In standard fMRI methods, the large voxel-size, along with
the combined effects of partial-voluming, pooling due to shared
vasculature [12], imperfect alignment during normalization and
group averaging, and spatial smoothing all conspire to bias fMRI
analyses towards findings of spurious overlap [13].
Mitchell [14] recently conducted the first direct comparison of
the Theory of Mind and exogenous attention tasks in the same
individuals. He reported overlap between the regions of RTPJ
recruited by the two tasks. For the reasons described above, these
results are potentially important but hard to interpret. We
therefore investigated whether there is real overlap between
regions recruited for Theory of mind and attentional reorienting,
and/or whether there is a reliable spatial separation between the
regions activated by the two tasks, using higher field strength (3T
versus 1.5T) and higher resolution (1.661.662.4 mm voxels,
versus 3.7563.7566 mm) than the previous study. To overcome
the limitations of the weak attention effect, we use a nonparametric bootstrap to estimate confidence intervals for the
relative locations of the peaks in group data. The bootstrap is a
computationally intensive method for assessing the uncertainty in
a statistical measure [15].
In fMRI studies, a cortical region in the right temporo-parietal
junction (RTPJ) is recruited when participants read stories about
people’s thoughts, relative to controls for logical and attentional
demands [1,2]. The blood oxygen level dependent (BOLD)
response in this region is significantly higher when participants
read about false beliefs than during closely-matched stories about
false maps or signs [3], and is specific to thinking about thoughts
relative to other social information [4–6].
Both fMRI and lesion studies also suggest that a region near the
RTPJ is involved in attentional reorienting. In a Posner spatialcueing paradigm [7], activity in the RTPJ is increased on
‘‘invalidly-cued’’ trials, when a target appears in an unexpected
location [8]. Similar activation has been observed during detection
of centrally presented low-frequency targets [9], suggesting that
the function of this region is in exogenously-cued redeployment of
attention. Damage to RTPJ is the most common cause of left
hemifield spatial neglect [10,11].
One obvious question is: do these findings reflect recruitment of
a single neural region, common to both Theory of Mind and
exogenous attention, or are there neighboring but distinct regions
within the RTPJ that account for these results? The discovery that
these two tasks share a common mechanism would be surprising
and informative, suggesting that the two tasks rely on common
psychological component process.
Caution is clearly necessary before we accept an observation that
two different tasks activate the ‘‘same’’ brain region in imaging
studies as evidence that common psychological mechanisms are
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Methods
Twenty-one naive adults (ten women, eleven men) participated
in the study for payment. Participants gave written consent, as
approved by the Committee on the Use of Humans as
Experimental Subjects at the Massachusetts Institute of Technology. All subjects were right-handed, native English speakers, and
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had normal or corrected-to-normal vision. Subjects were scanned
using 12-channel head coil, in a 3T Siemens MAGNETOM Trio,
a TIM system (Siemens Medical Solutions, Erlangen, Germany)
in the Athinoula A. Martinos Imaging Center at the McGovern
Institute for Brain Research at MIT. Functional BOLD images
were collected using a single shot gradient echo echo-planar
imaging (EPI) sequence with TR = 2s, TE = 30 ms, flip angle = 90u. Twenty-one 2-mm thick near-axial slices were
acquired with 20% interslice gap and in-plane resolution of
1.661.6 mm, providing coverage over superior temporal and
parietal cortices.
Runs of two experiments were interleaved in a single scan
session for each participant. In the Theory of Mind experiment,
subjects read 24 short vignettes about the formation of a
representation (12 about beliefs [‘Belief’ trials], 12 about physical
representations like a photo, drawing, or map [‘Photo’ trials]) that
did not correspond to a reality [2,Figure 1a]. Stories were on
average 32 words long, and were presented for 10 seconds.
Subjects then answered a fill-in-the-blank question either about
the representation or about reality (4 seconds). Stories from the
two conditions alternated (order counterbalanced across subjects
and runs), with 12s rest interleaved between each story. Each run
lasted 2 min and 48 seconds (six stories); each subject participated
in four runs of this experiment.
The spatial attention experiment followed the procedure
described by Corbetta [8], adapted from the Posner cueing task
[7]. Before each trial, the subject viewed a central fixation dot
flanked on either side by empty square boxes. At the start of a trial
the fixation dot turned green (Figure 1b), and was followed by a
cue arrow pointing to one side (left 50%, right 50%). The arrow
was followed in 44% of the trials by the appearance of the target (a
large asterisk) at the cued location (a ‘‘valid’’ trial), in another 16%
of the trials at the opposite location (an ‘‘invalid’’ trial), and in 20%
of trials, no target appeared (a ‘‘noise’’ trial). On the remaining
20% of trials, after the arrow appeared the central fixation turned
immediately back to red (‘‘cue’’ trial). Trials were separated by a
fixation interval of 9, 10 or 12 seconds (randomly distributed;
mean fixation duration 10 seconds). Trials from the four
conditions (valid, invalid, cue, noise) were randomly intermixed.
Each run was comprised of 25 trials, and each subject participated
in 4 to 6 runs of the experiment. Subjects were provided an MRsafe response box and instructed to respond with a single button
press as quickly as possible when a target appeared in either box
(effectively, a go/no-go task). This method isolates the effect of
‘‘invalid’’ trials to visuo-spatial attention; there is no response
conflict once the target has been detected.
Stimuli were presented onto a screen via Matlab 5.0 with an
Apple G4 powerbook on a dark background. Reaction time
measures were obtained during both experiments with the MRsafe button box. Behavioral data for three subjects in the Theory
of Mind task were lost due to technical error.
fMRI data were analyzed with SPM2 (Wellcome Department of
Cognitive Neurology, London, UK). The images were motion
corrected, normalized to a functional template, and smoothed with
a Gaussian filter (FWHM = 2.5 mm) prior to analysis; during
normalizing, the functional data were resliced to 26262 mm.
Data were then high-pass filtered and modeled using a boxcar
regressor to estimate the hemodynamic responses for each
condition (Belief, Photo; Invalid, Valid, Cue, Noise). Contrasts
from each task (Belief-Photo, Invalid-Valid) were calculated for
each subject and then combined for group random effects
analyses. Surface reconstructions, for the figures, were constructed
for the group average results based on a standard single subject
anatomical.
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Figure 1. Task structure for the two tasks. (A) Sample stimuli for
the Theory of Mind task. The key contrast compares the brain response
while participants read ‘Belief’ vs ‘Photo’ stories. (B) Four trial types in
the attentional reorienting task. The key contrast compared the brain
response while participants detect the target on Invalid, vs Valid, trials.
doi:10.1371/journal.pone.0004869.g001
An analysis of overlap between activations was conducted on
group random effects data using in-house software. Overlap was
calculated as a function of threshold on the voxel-wise T statistic
for the two contrasts. For the purposes of the overlap analyses, an
anatomical ROI was defined as a cube around the peaks from the
two group averages: [X 40:66; Y 273:241; Z 20:52]. Active
voxels were defined as any voxel passing the threshold in either
task that fell inside the anatomical ROI. Overlap was measured as
a strict conjunction: each voxel counted as ‘overlap’ only if the
contrast exceeded the T-threshold independently for both tasks.
Percentage overlap was defined as the percentage of active voxels
that were counted in the conjunction. For technical reasons, in the
bootstrap and individual subjects overlap analyses there was no
extent threshold applied, whereas in the group analyses used an
extent threshold of k.10; as a result the bootstrap analysis was
more likely to find overlapping voxels, and therefore provides an
upper bound on the true percentage overlap.
The spatial relationship between activations was assessed with a
repeated-sample random effects analysis using a non-parametric
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bootstrap. In this procedure the data were randomly sampled, with
replacement, to create 150 bootstrap samples (each with n = 21).
Random effects analyses were generated with each of these
samples for each task contrast. Peak voxel coordinates were
selected automatically for each sample as the voxel with the
maximum T-statistic within an 18 mm radius of the anatomicallydefined centre of the RTPJ.
Results
Behavioral Results
Reaction times during the attention task were faster on valid
(mean 408 ms) than invalid trials (445 ms) using a paired-samples
t-test, confirming that subjects were influenced by the spatial cue
(t(20) = 3.8,p,0.001). Participants’ responses to Belief trials
(2.64 s) were faster to Photo trials (2.80 s) during the Theory of
Mind task (t(17) = 2.7,p = 0.015).
fMRI Results
Consistent with previous results, random effects group analyses
revealed regions near the right temporo-parietal junction recruited
during the Theory of Mind task (Belief.Photo, peak voxel: 60
256 32 (MNI), cluster size: 320 voxels (2.56 cm3) at p,0.001,
peak T = 7.5) and the spatial attention task (Invalid.Valid, peak
voxel: 58 262 42, cluster size: 16 voxels (0.13 cm3) at p,0.001,
peak T = 5.9, Figure 2a). Each of these peaks was somewhat higher
than the average peaks reported by Mitchell [14] and Decety and
Lamm [16], who for example report peaks for Attention at [52
250 28], and for Theory of Mind at [52 252 18]. This difference
within the range of inter-subject variability in anatomy [9], and
illustrates the need for within subject comparisons across tasks to
reveal their true functional relationships.
In individual subjects, a cluster of k.10 contiguous voxels, at a
threshold of p,0.001, was identified near the RTPJ in 18/21
individual subjects for Belief.Photo, and 12/21 individuals for
Invalid.Valid. Eleven individuals had clusters in both contrasts;
in those individuals, the average cluster size for Belief.Photo was
268 voxels (2.15 cm3), and for Invalid.Valid was 52 voxels
(0.42 cm3).
The current design allowed us to ask four questions about the
relationship between brain regions recruited during the Theory of
Mind and attention tasks. First, can we replicate the prior finding
that the RTPJ region implicated in each task does significantly
differentiate, on average, the critical trials of the other task?
Second, is there a region significantly recruited for the conjunction
of both tasks (i.e. is there any real overlap)? Third, within the
regions recruited by each task, are common or distinct subpopulations of neurons driving the responses to the two tasks?
Finally, is there a reliable spatial separation between the peak
activations for the two tasks? To answer these questions, we
analyzed the results in the group average results, using a bootstrap
to estimate confidence intervals, and separately in the eleven
individual subjects in whom both contrasts yielded detectable
activations.
ROI analyses – Group Results. We defined functional
regions of interest for each task in group data and then examined
the functional response to the other task in these regions. The ROI
defined by the Theory of Mind task did differentiate between
invalid and valid trials of the attention task (t(20) = 4.9,p,0.0001),
and the ROI defined by the attention task differentiated between
belief and photo trials of the Theory of Mind task
(t(20) = 3.8,p,0.001), consistent with previous results [14].
ROI analyses – Individual Subjects. We also conducted an
ROI analysis in the eleven individual subjects who showed an
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Figure 2. Group Activations. (A) Group activations for Belief – Photo
(Red) and Invalid – Valid (Green), both p,0.001 uncorrected, k.5. (B)
Group activations with approximately matching numbers of voxels:
Belief – Photo (Red, 82 voxels, p,0.0001, k.10) and Invalid – Valid
(Green, 72 voxels, p,0.005, k.10). (C) Interaction of the two contrasts,
((Belief-Photo)-(Invalid-Valid)), p,0.01, k.10. All activations were
superimposed on the inflated T1 canonical brain in SPM using the
SurfRend toolbox.
doi:10.1371/journal.pone.0004869.g002
exogenous attention effect. In these data we observed the same
pattern that we found in the group data, but with smaller effects.
The ROI defined by the Theory of Mind task differentiated
between the invalid and valid trials of the attention task
(t(10) = 3.3,p,0.01), and the ROI defined by the attention task
differentiated belief and photo trials of the Theory of Mind task
(t(10) = 3.9,p,0.01) (Figure 3).
Overlap – Group Results. In order to determine the
amount of overlap between the group average activations, we
calculated the number of voxels in the group whole brain contrast
recruited for both tasks as a function of statistical threshold. For
T.3.5, p,0.001, there were 7 voxels in the conjunction,
accounting for 2% of active voxels. This percentage decreased
with increasingly strict T-thresholds as long as the number of
overlapping voxels was non-zero. We also calculated the overlap
between these two activations after correcting for the difference in
size between the two regions. The Belief versus Photo contrast was
set to a threshold of p,0.0001, k.10, and the Invalid versus Valid
was set to a threshold of p,0.005, k.10. These thresholds
produced activations including 82 voxels (0.66 cm3) in the Theory
of mind region, and 72 voxels (0.58 cm3) in the attention region.
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Figure 4. Percent signal change in individually defined regions
of interest (n = 11). Bars represent standard error of the mean.
doi:10.1371/journal.pone.0004869.g004
Cross-Voxel Correlations – Group Results. Peelen et al
[17] recently introduced a cross-voxel correlation analysis,
specifically designed to examine whether two functional
responses within a single fMRI region arose from common or
distinct neural sub-populations. In this analysis, the t-value for
each contrast was extracted from each individual voxel within the
region of interest. If the t-values from two contrasts were positively
correlated across voxels, then the two contrasts were inferred to
share common sub-populations of neurons within the region. In
the size-matched ROIs defined by the Group Results, we found no
evidence for a cross-voxel correlation between the t-values for the
Belief-Photo and Invalid-Valid contrasts (Theory of Mind region,
r2 = 0.03; Attention region, r2 = 0.01).
Spatial Separation – Bootstrap. In the group random
effects analyses, the voxel most significantly recruited by the belief
task was 2 mm lateral, 6 mm anterior, and 10 mm inferior to the
peak voxel in the attention task. The bootstrap revealed that only
the spatial displacement in the Z (inferior-superior) dimension was
reliable. There was no reliable difference between the location of
the two peaks in the X (medial - lateral) or Y (anterior-posterior)
dimensions (confidence intervals on the differences included 0).
However, the peak for the attention task was reliably superior to
the peak for the belief task (99.9% confidence interval of the mean
for the z-coordinate of the attention task: 38–42 mm; of the belief
task 31–33 mm; 99.9% confidence interval for the mean
difference between the peaks within a sample: 6–10 mm, Figure 4).
Spatial Separation – Individual Subjects. In the 11
individual subjects in whom clusters could be identified for both
contrasts, at p,0.001, the average peak of the cluster in
Belief.Photo was at [57 263 33]; in the same subjects, the
average peak of Invalid.Valid was at [55 260 41]. The peak of
the attention cluster was on average 9 mm superior to the peak of
the Theory of Mind cluster in the same individual (t(10) = 2.6,
p = 0.03, paired-samples t-test).
Figure 3. Spatial Separation. (A) Distance between the peaks
estimated by the bootstrap: attention region – Theory of Mind region.
Bars show 99.9% confidence intervals (3.36 standard errors of the
bootstrap mean). There was no reliable difference in the X (medial to
lateral) or Y (anterior to posterior) axes, but the attention region was
reliably 6–10 mm superior to the theory of mind region. (B) Histogram
of the observed distance, on the Z dimension, between the Theory of
Mind and attention regions, in the 150 non-parametric bootstrap
samples. Positive values indicate that the attention peak was superior
to the Theory of Mind peak.
doi:10.1371/journal.pone.0004869.g003
Using these criteria, 5% of active voxels passed both thresholds
(Figure 2b).
Overlap – Bootstrap. In order to estimate confidence
intervals for the percent overlap, we calculated the percent
overlap between the two activations, at T.3.5, p,0.001, k.0, in
each bootstrap sample. This analysis yielded a 99.9% confidence
interval of 6–8% mean overlap.
Overlap – Individual Subjects. For each of the eleven
individuals in whom we could identify a cluster in both contrasts at
p,0.001, k.0, we calculated the percent of active voxels that were
in the conjunction. The average percent overlap in these individual
subjects was 3%; the range was from zero overlap to 10% overlap.
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Discussion
The specific question at the center of this paper is whether one
common neural substrate near the RTPJ is recruited both during
the Theory of Mind and exogenous attention tasks. Ideally, any
substantive claim for a common neural mechanism requires
evidence of substantial overlap and no reliable spatial separation
between the regions activated by the two tasks, in individual
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subjects’ brains. These standards of evidence are hard to achieve
in the current case, because the region recruited by attentional
reorienting is smaller and less reliable in individual subjects that
then region recruited during Theory of Mind tasks [2,14]. At any
statistical threshold, partial overlap might be evidence that the two
tasks recruit the same region to different degrees, or that the two
tasks recruit neighboring but distinct neural populations whose
peripheries are partly overlapping. To overcome these limitations,
we used three analysis strategies: a direct comparison of the two
regions in the eleven individuals in whom both regions could be
identified, a cross-voxel correlation analysis within each region of
interest, and group analyses using a bootstrap to estimate
confidence intervals. All three analyses converged to suggest that
Theory of Mind and attentional reorienting recruit distinct cortical
regions near the RTPJ.
Mitchell [14] recently reported that the activations associated
with the two tasks, in group analyses, were substantially
overlapping, and that ROIs defined by the Belief-Photo contrast
significantly differentiated between Invalid and Valid trials of the
attention task. We replicated this latter result in the ROIs, but we
found much less evidence of overlap in direct tests. Specifically,
cross-voxel correlation analyses suggested that distinct subpopulations within each region were driving the responses to the
Belief-Photo, and Invalid-Valid, contrasts. The ROI average
results may therefore reflect ‘‘bleed’’ of the functional response
between two nearby regions, or neural populations. In an ROI
analysis, the functional responses of all voxels in the region are
averaged together, so relatively few voxels with an overlapping
response may be sufficient to generate a significantly different
average response. This is also consistent with the observation that
the magnitude of the attention effect was small in the belief region,
and vice versa (Figure 3), and with strict conjunction analyses
suggesting that the overlap between the two regions is relatively
small and at the periphery of the two activations (Figure 2).
Given these results, we suggest that the substantial overlap
observed in the previous study may have been partly due to partial
voluming effects in lower resolution data. Distinctions between
nearby functional regions that are conflated in low resolution data
can often be differentiated at higher resolution (e.g. [12,18]). For
example, response inhibition tasks yield bilateral activation in the
anterior cingulate at low resolution (26264 mm) but strongly
right lateralized activation at high-resolution (1.5 mm isotropic,
[19]. In the current study, the separation between the peaks of the
two regions was estimated to be 6–10 mm, approximately two
voxels at the resolution of the previous paper (3.7563.7566 mm,
[14], making these regions nearly impossible to resolve. The
higher resolution we used (1.661.662.4 mm) was probably a key
factor making the difference between the current and previous
conclusions in the overlap analyses.
One challenge for overlap analyses, though, is that the two
functional contrasts may not be matched in power. In fact, it is
unclear how to compare the power of the two experiments. Some
considerations favor the attention effect. We measured the
response to 16–24 invalid attention trials per individual, and only
12 belief trials per individual, allowing for a more accurate
estimate of the amplitude of the response to invalid vs. valid trials;
and our temporal model for the invalid cue (the onset of the target)
was more precise than for the onset of belief representation,
allowing for better prediction of the hemodynamic response in the
attention task. On the other hand, the sentences implying a
character’s beliefs were presented for 10 seconds, whereas the
target in the attention task was presented for less than a second.
Since the latency, reliability, and duration of the two cognitive
processes (detecting that a cue was invalid, constructing a belief
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representation) are both unknown, a precise estimate of the
relative power of the two experiments is hard to derive. In
subsequent analyses, we therefore analyzed the position rather
than the extent of these activations. Because this approach utilized
only peak coordinates it was relatively immune to differences in
power across the experiments.
We used a non-parametric bootstrap procedure to estimate the
relative spatial positions of the two regions of activation (Figure 3).
The voxels showing the strongest response in the two tasks, in the
group Random Effects analyses, were separated by 266610 mm.
However, traditional group analyses do not provide a way to
estimate confidence intervals for this measurement. The bootstrap
technique is particularly useful for estimating the distribution of a
statistic in a situation like this one in which a measurement can be
obtained from a group average but not from individuals.
Bootstrapping is a simple, widely used, but computationally
intensive method for estimating uncertainty in statistics of interest
[20,21]. Although quite common in the broader science
community, to our knowledge this is the first time the bootstrap
has been used to estimate confidence in the location of peak
activations in fMRI data (but see [22]).
Non-parametric samples for the bootstrap are constructed by
sampling randomly with replacement from the population; the
average peak location (or other statistic) is then calculated for each
task contrast. Since individuals from the original sample contribute
differentially to each bootstrap sample (i.e. in any one bootstrap
sample of size n, each of the original individuals is represented
between zero and n times), the variability of the means across the
bootstrap samples provide an estimate of the variability across
individuals in the original sample. Efron and Tibshirani [15]
report that 25 to 200 bootstrap samples may be required to
accurately compute the bootstrap estimate of the standard error of
a parameter like the peak locations in the current study; we used
150 samples. This technique proved very useful. We were able to
estimate that while the observed separation between the peaks in
the anterior-posterior and medial-lateral axes was not reliably
greater than zero, there was a highly reliable segregation of the
peaks of the two regions in the inferior-superior axis. The same
direction and magnitude of separation was apparent in the eleven
individual subjects in whom activated clusters could be detected in
both contrasts.
The inferior-superior segregation of the two activations in the
current data is also consistent with the results of previous studies.
First, Mitchell [14] reported that the average peak of the Theory
of Mind regions in three previous papers was [56 254 19], and the
average peak of the attention region in five previous papers was
[55 250 26]. The attention region was therefore on average 7 mm
superior to the belief region, consistent with the distance estimated
by our bootstrap. Second, Decety and Lamm [16] recently
conducted a meta-analysis of seventy previously published group
analyses of attention and Theory of Mind. Again, the authors
reported that attention tasks produced an average peak activation
10 mm superior to the average peak of Theory of Mind tasks.
Decety and Lamm [16] concluded that a spatial separation of
10 mm was consistent with a single underlying cortical region.
However, the convergence of the meta-analysis with our current
results is more consistent with a real dissociation between two
distinct regions.
In sum, Theory of Mind and exogenous attention appear to
recruit neighboring but distinct regions of cortex. These results are
consistent with the (intuitive) idea that these tasks do not share a
common cognitive component process - although it is of course still
possible that these two regions are neighboring because they are
functionally or ontogenetically related to one another.
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Table 1. Direct comparison of the activations for Theory of Mind and attentional reorienting.
Group Average
Bootstrap Estimate
Individual Subjects
Percent Overlap, both p,0.001
2%
6–8%
3%
Distance between Peaks
10 mm
6–10 mm
9 mm
Overlap and spatial separation between the Theory of Mind and attention regions, computed in the group average, using a non-parametric bootstrap to estimate
confidence intervals, and in eleven individual subjects.
doi:10.1371/journal.pone.0004869.t001
Note that prior data already made it very unlikely that the
results of either paradigm included a confound of the other. The
attention task is does not covertly depend on Theory of Mind [14].
RTPJ recruitment has been observed for exogenous attention tasks
that don’t involve any ‘‘false cueing’’ manipulation [9]. Similarly,
the results observed in Theory of Mind tasks are not confounded
with shifts of exogenous attention. False photographs and false
maps provide a well-matched control for false beliefs in terms of
logical and inhibitory demands, as well as reading times and
syntactic complexity, but do not recruit the RTPJ [2,3,23]. The
response of the RTPJ for Theory of Mind tasks generalizes from
verbal to pictorial stimuli [24], and from visual to aural
presentation (Bedny et al, in preparation), each of which creates
different attentional demands. The initial response of the RTPJ is
specific in time to the moment when a belief is presented, but
independent of both the truth-value and the emotional valence of
the belief content [5,25]; that is, the response is equally high for
true and false beliefs, for negatively- or positively-valenced beliefs,
and for beliefs shared or not shared by the participant. Finally,
even when the stimuli and the subjects’ responses are all physically
identical, just changing the task instructions from an abstract rule
to answering a question about a person’s thoughts is sufficient to
elicit enhanced recruitment of the RTPJ [6]. Overall, this profile
cannot be explained away in terms of attentional shifts; and
instead suggests a neural mechanism involved in thinking about
thoughts and beliefs.
The question for the current paper was therefore not whether
the tasks used in prior studies were confounded. Instead, evidence
of a common neural region would have suggested the presence of a
component process, not evident from intuitive task analyses, but
shared by both tasks. In principle, this kind of evidence could be
an important contribution of fMRI to cognitive science. However,
the current results illustrate some of the challenges for establishing
that two dissimilar tasks share a common neural substrate based
on overlapping activations in fMRI data. The low resolution of
typical fMRI data relative to the true functional resolution of
cortex, along with the combined effects of partial voluming,
pooling due to shared vasculature [12], distortions during
normalization for group averaging, and spatial smoothing all
conspire to bias fMRI analyses towards findings of spurious
overlap. In the specific case under investigation here, the regions
of RTPJ implicated in exogenous attention and Theory of Mind,
our results suggest that the regions recruited for the two tasks are
nearby but distinct, and consequently there is no need to posit a
common psychological mechanism.
Acknowledgments
The authors thank Chen Zhao and Steve Lee for stimulus construction and
a pilot study, Mira Bernstein for advice on statistics, and Nancy Kanwisher
for comments on an earlier draft of the manuscript. Scanning was
conducted at the Athinoula A. Martinos Imaging Center at McGovern
Institute for Brain Research, MIT.
Author Contributions
Conceived and designed the experiments: JKS RS. Performed the
experiments: JKS CT RS. Analyzed the data: JKS SWG RS. Contributed
reagents/materials/analysis tools: CT SWG ENB. Wrote the paper: JKS
RS.
References
1. Fletcher PC, Happé F, Frith U, Baker SC, Dolan RJ, et al. (1995) Other minds
in the brain: a functional imaging study of ‘‘theory of mind’’ in story
comprehension. Cognition 57: 109–128.
2. Saxe R, Kanwisher N (2003) People thinking about thinking people: the role of
the temporo-parietal junction in theory of mind. Neuroimage 19: 1835–1842.
3. Perner J, Aichhorn M, Kronbichler M, Staffen W, Ladurner G (2006) Thinking
of mental and other representations: The roles of left and right temporo-parietal
junction. Social Neuroscience 1: 245–258.
4. Gobbini MI, Koralek AC, Bryan RE, Montgomery KJ, Haxby JV (2007) Two
Takes on the Social Brain: A Comparison of Theory of Mind Tasks. Journal of
Cognitive Neuroscience 19: 1803–1814.
5. Saxe R, Wexler A (2005) Making sense of another mind. The role of the right
temporo-parietal junction. Neuropsychologia 43: 1391–1399.
6. Saxe R, Powell LJ (2006) It’s the thought that counts: specific brain regions for
one component of theory of mind. Psychological Science 17: 692–699.
7. Posner MI, Walker JA, Friedrich FJ, Rafal RD (1984) Effects of parietal injury
on covert orienting of attention. Journal of Neuroscience 4: 1863–1874.
8. Corbetta M, Kincade JM, Ollinger JM, McAvoy MP, Shulman GL (2000)
Voluntary orienting is dissociated from target detection in human posterior
parietal cortex. Nat Neurosci 3: 292–297.
9. Devlin JT, Poldrack RA (2007) In praise of tedious anatomy. Neuroimage 37:
1033–1041.
10. Valler G, Perani D (1986) The anatomy of unilateral neglect after righthemisphere stroke lesions. A clinical/CT-scan correlation study in man.
Neuropsychologia 24: 609–622.
PLoS ONE | www.plosone.org
11. Vallar G, Bottini G, Rusconi ML, Sterzi R (1993) Exploring somatosensory
hemineglect by vestibular stimulation. Brain 116: 71.
12. Baker N (2004) Functional selectivity of human extrastriate visual cortex at high
resolution. Journal of Vision 4: 88.
13. Huettel SA, McKeown MJ, Song AW, Hart S, Spencer DD, et al. (2004) Linking
hemodynamic and electrophysiological measures of brain activity: evidence from
functional MRI and intracranial field potentials. Cerebral Cortex 14: 165–173.
14. Mitchell JP (2008) Activity in right temporo-parietal junction is not selective for
theory-of-mind. Cerebral Cortex 18: 262.
15. Efron B, Tibshirani RJ (1993) An introduction to the boostrap. Monographs on
Statistics and Applied Probability 57.
16. Decety J, Lamm C (2007) The role of the right temporoparietal junction in social
interaction: how low-level computational processes contribute to meta-cognition.
The Neuroscientist 13: 580.
17. Peelen MV, Wiggett AJ, Downing PE (2006) Patterns of fMRI activity dissociate
overlapping functional brain areas that respond to biological motion. Neuron 49:
815–822.
18. Schwarzlose RF, Baker CI, Kanwisher N (2005) Separate face and body
selectivity on the fusiform gyrus. J Neurosci 25: 11055–11059.
19. Lütcke H, Frahm J (2008) Lateralized anterior cingulate function during error
processing and conflict monitoring as revealed by high-resolution fMRI. Cereb
Cortex 18: 508–515.
20. Bullmore E, Horwitz B, Honey G, Brammer M, Williams S, et al. (2000)
how good is good enough in path analysis of fMRI data? Neuroimage 11:
289–301.
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Distinct Regions of RTPJ
24. Gallagher HL, Happé F, Brunswick N, Fletcher PC, Frith U, et al. (2000)
Reading the mind in cartoons and stories: an fMRI study of ‘‘theory of mind’’ in
verbal and nonverbal tasks. Neuropsychologia 38: 11–21.
25. Saxe R, Schulz LE, Jiang YV (2006) Reading minds versus following rules:
dissociating theory of mind and executive control in the brain. Social
Neuroscience 1: 284–298.
21. Pajevic S, Basser PJ (2003e) Parametric and non-parametric statistical analysis of
DT-MRI data. Journal of Magnetic Resonance 161: 1–14.
22. Auffermann WF, Ngan SC, Hu X (2002) Cluster significance testing using the
bootstrap. Neuroimage 17: 583–591.
23. Zaiichik D (1990) When representations conflict with reality: the preschooler’s
problem with false beliefs and false photographs. Cognition 35: 41–68.
PLoS ONE | www.plosone.org
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